Object recognition is an important aspect of robotic vision systems. In a manufacturing environment, robots are often required to detect the pose of a part or tool. Because of variations in the appearance of the part due to light conditions or other factors, consistently recognizing the part's pose can be difficult.
Several techniques have been employed in the field of computer vision systems to enable recognition of an object under varying conditions. U.S. Pat. No. 6,026,189 to Greenspan discloses a method for recognizing objects within an image that utilizes a data tree that stores, as nodes on the tree, surface features of the object being recognized. To determine if a detected object is the desired object, the tree is searched by evaluating whether the detected object meets the criteria of a node and proceeding along the tree based on the decision at each node. This technique matches three dimensional range data to stored three dimensional maps and can be memory intensive.
U.S. Pat. No. 6,477,275 to Melikian et al. discloses another technique for locating an object in an image. In the Melikian method, a template for the desired object is shifted over sub portions of the image and a correlation of the sub portion to the image is measured. This technique requires grey scale processing capabilities. U.S. patent No. to Montillo et al. discloses a method for determining the exact location of pads on a printed circuit board once an approximate position on the circuit board has been input by an operator. The Montillo technique is relatively simple, and compares the locations of predetermined “anchor points” on the circuit board being tested with stored locations.